Communication Efficient Distributed Estimation over Directed Random Graphs

Abstract

© 2019 IEEE. Recently, a communication efficient recursive distributed estimator, CREDO, has been proposed, that utilizes increasingly sparse randomized bidirectional communications. < p> CREDO achieves order-optimal O(1/t) mean square error (MSE) rate in the number of per-node processed samples t, and a < p> O(1/Ct2-ζ) MSE rate in the number of per-node communications, where ζ > 0 is arbitrarily small. In this paper, we present directed C R E D O, D-CREDO for short-a distributed recursive estimator that utilizes directed increasingly sparse communications. We show that D-CREDO further dramatically improves communication efficiency, achieving the O(1/cT) communication MSE rate with arbitrarily high exponent κ, while keeping the order-optimal O(1/t) sample-wise MSE rate. Numerical examples on real data sets confirm our results.